Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations2315
Missing cells2305
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory364.1 KiB
Average record size in memory161.1 B

Variable types

Categorical7
Text11
DateTime1
Numeric1
Boolean1

Alerts

discipline is highly overall correlated with event_type and 1 other fieldsHigh correlation
event_type is highly overall correlated with disciplineHigh correlation
gender is highly overall correlated with team_genderHigh correlation
medal_code is highly overall correlated with medal_typeHigh correlation
medal_type is highly overall correlated with medal_codeHigh correlation
team_gender is highly overall correlated with discipline and 1 other fieldsHigh correlation
is_medallist is highly imbalanced (85.7%) Imbalance
team has 760 (32.8%) missing values Missing
team_gender has 760 (32.8%) missing values Missing
code_team has 760 (32.8%) missing values Missing

Reproduction

Analysis started2025-03-12 19:35:50.926063
Analysis finished2025-03-12 19:35:51.384183
Duration0.46 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

medal_date
Categorical

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2024-08-10
371 
2024-08-09
276 
2024-08-03
244 
2024-08-08
180 
2024-08-11
178 
Other values (11)
1066 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters23150
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-07-27
2nd row2024-07-27
3rd row2024-07-27
4th row2024-07-27
5th row2024-07-27

Common Values

ValueCountFrequency (%)
2024-08-10 371
16.0%
2024-08-09 276
11.9%
2024-08-03 244
10.5%
2024-08-08 180
 
7.8%
2024-08-11 178
 
7.7%
2024-08-07 124
 
5.4%
2024-08-04 121
 
5.2%
2024-08-02 120
 
5.2%
2024-07-30 113
 
4.9%
2024-07-27 112
 
4.8%
Other values (6) 476
20.6%

Length

2025-03-12T16:35:51.400950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-08-10 371
16.0%
2024-08-09 276
11.9%
2024-08-03 244
10.5%
2024-08-08 180
 
7.8%
2024-08-11 178
 
7.7%
2024-08-07 124
 
5.4%
2024-08-04 121
 
5.2%
2024-08-02 120
 
5.2%
2024-07-30 113
 
4.9%
2024-07-27 112
 
4.8%
Other values (6) 476
20.6%

Most occurring characters

ValueCountFrequency (%)
0 6433
27.8%
2 4998
21.6%
- 4630
20.0%
4 2436
 
10.5%
8 2095
 
9.0%
1 912
 
3.9%
7 683
 
3.0%
3 443
 
1.9%
9 365
 
1.6%
5 99
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6433
27.8%
2 4998
21.6%
- 4630
20.0%
4 2436
 
10.5%
8 2095
 
9.0%
1 912
 
3.9%
7 683
 
3.0%
3 443
 
1.9%
9 365
 
1.6%
5 99
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6433
27.8%
2 4998
21.6%
- 4630
20.0%
4 2436
 
10.5%
8 2095
 
9.0%
1 912
 
3.9%
7 683
 
3.0%
3 443
 
1.9%
9 365
 
1.6%
5 99
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6433
27.8%
2 4998
21.6%
- 4630
20.0%
4 2436
 
10.5%
8 2095
 
9.0%
1 912
 
3.9%
7 683
 
3.0%
3 443
 
1.9%
9 365
 
1.6%
5 99
 
0.4%

medal_type
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Bronze Medal
807 
Silver Medal
756 
Gold Medal
752 

Length

Max length12
Median length12
Mean length11.350324
Min length10

Characters and Unicode

Total characters26276
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGold Medal
2nd rowSilver Medal
3rd rowBronze Medal
4th rowGold Medal
5th rowSilver Medal

Common Values

ValueCountFrequency (%)
Bronze Medal 807
34.9%
Silver Medal 756
32.7%
Gold Medal 752
32.5%

Length

2025-03-12T16:35:51.428099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T16:35:51.452174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medal 2315
50.0%
bronze 807
 
17.4%
silver 756
 
16.3%
gold 752
 
16.2%

Most occurring characters

ValueCountFrequency (%)
e 3878
14.8%
l 3823
14.5%
d 3067
11.7%
2315
8.8%
M 2315
8.8%
a 2315
8.8%
r 1563
5.9%
o 1559
5.9%
B 807
 
3.1%
n 807
 
3.1%
Other values (5) 3827
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3878
14.8%
l 3823
14.5%
d 3067
11.7%
2315
8.8%
M 2315
8.8%
a 2315
8.8%
r 1563
5.9%
o 1559
5.9%
B 807
 
3.1%
n 807
 
3.1%
Other values (5) 3827
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3878
14.8%
l 3823
14.5%
d 3067
11.7%
2315
8.8%
M 2315
8.8%
a 2315
8.8%
r 1563
5.9%
o 1559
5.9%
B 807
 
3.1%
n 807
 
3.1%
Other values (5) 3827
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3878
14.8%
l 3823
14.5%
d 3067
11.7%
2315
8.8%
M 2315
8.8%
a 2315
8.8%
r 1563
5.9%
o 1559
5.9%
B 807
 
3.1%
n 807
 
3.1%
Other values (5) 3827
14.6%

medal_code
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size18.2 KiB
3.0
806 
2.0
756 
1.0
752 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6942
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 806
34.8%
2.0 756
32.7%
1.0 752
32.5%
(Missing) 1
 
< 0.1%

Length

2025-03-12T16:35:51.474281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T16:35:51.488959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 806
34.8%
2.0 756
32.7%
1.0 752
32.5%

Most occurring characters

ValueCountFrequency (%)
. 2314
33.3%
0 2314
33.3%
3 806
 
11.6%
2 756
 
10.9%
1 752
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2314
33.3%
0 2314
33.3%
3 806
 
11.6%
2 756
 
10.9%
1 752
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2314
33.3%
0 2314
33.3%
3 806
 
11.6%
2 756
 
10.9%
1 752
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2314
33.3%
0 2314
33.3%
3 806
 
11.6%
2 756
 
10.9%
1 752
 
10.8%

name
Text

Distinct2053
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2025-03-12T16:35:51.570436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length30
Mean length14.051404
Min length3

Characters and Unicode

Total characters32529
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1852 ?
Unique (%)80.0%

Sample

1st rowEVENEPOEL Remco
2nd rowGANNA Filippo
3rd rowvan AERT Wout
4th rowBROWN Grace
5th rowHENDERSON Anna
ValueCountFrequency (%)
van 31
 
0.6%
de 25
 
0.5%
wang 19
 
0.4%
lee 17
 
0.3%
kim 17
 
0.3%
yang 16
 
0.3%
thomas 16
 
0.3%
sarah 16
 
0.3%
matthew 14
 
0.3%
smith 14
 
0.3%
Other values (3398) 4754
96.3%
2025-03-12T16:35:51.687598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2624
 
8.1%
A 1980
 
6.1%
a 1929
 
5.9%
E 1553
 
4.8%
i 1344
 
4.1%
e 1338
 
4.1%
N 1271
 
3.9%
I 1211
 
3.7%
n 1207
 
3.7%
R 1198
 
3.7%
Other values (48) 16874
51.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2624
 
8.1%
A 1980
 
6.1%
a 1929
 
5.9%
E 1553
 
4.8%
i 1344
 
4.1%
e 1338
 
4.1%
N 1271
 
3.9%
I 1211
 
3.7%
n 1207
 
3.7%
R 1198
 
3.7%
Other values (48) 16874
51.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2624
 
8.1%
A 1980
 
6.1%
a 1929
 
5.9%
E 1553
 
4.8%
i 1344
 
4.1%
e 1338
 
4.1%
N 1271
 
3.9%
I 1211
 
3.7%
n 1207
 
3.7%
R 1198
 
3.7%
Other values (48) 16874
51.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2624
 
8.1%
A 1980
 
6.1%
a 1929
 
5.9%
E 1553
 
4.8%
i 1344
 
4.1%
e 1338
 
4.1%
N 1271
 
3.9%
I 1211
 
3.7%
n 1207
 
3.7%
R 1198
 
3.7%
Other values (48) 16874
51.9%

gender
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Female
1162 
Male
1153 

Length

Max length6
Median length6
Mean length5.0038877
Min length4

Characters and Unicode

Total characters11584
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 1162
50.2%
Male 1153
49.8%

Length

2025-03-12T16:35:51.716550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T16:35:51.733228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 1162
50.2%
male 1153
49.8%

Most occurring characters

ValueCountFrequency (%)
e 3477
30.0%
a 2315
20.0%
l 2315
20.0%
F 1162
 
10.0%
m 1162
 
10.0%
M 1153
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3477
30.0%
a 2315
20.0%
l 2315
20.0%
F 1162
 
10.0%
m 1162
 
10.0%
M 1153
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3477
30.0%
a 2315
20.0%
l 2315
20.0%
F 1162
 
10.0%
m 1162
 
10.0%
M 1153
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3477
30.0%
a 2315
20.0%
l 2315
20.0%
F 1162
 
10.0%
m 1162
 
10.0%
M 1153
 
10.0%
Distinct92
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2025-03-12T16:35:51.781411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6945
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.6%

Sample

1st rowBEL
2nd rowITA
3rd rowBEL
4th rowAUS
5th rowGBR
ValueCountFrequency (%)
usa 330
 
14.3%
fra 187
 
8.1%
chn 168
 
7.3%
gbr 162
 
7.0%
aus 123
 
5.3%
ned 118
 
5.1%
ger 113
 
4.9%
ita 88
 
3.8%
esp 83
 
3.6%
jpn 82
 
3.5%
Other values (82) 861
37.2%
2025-03-12T16:35:51.854889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 982
14.1%
R 838
12.1%
S 634
 
9.1%
N 624
 
9.0%
U 599
 
8.6%
E 457
 
6.6%
G 335
 
4.8%
B 310
 
4.5%
C 280
 
4.0%
P 226
 
3.3%
Other values (16) 1660
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 982
14.1%
R 838
12.1%
S 634
 
9.1%
N 624
 
9.0%
U 599
 
8.6%
E 457
 
6.6%
G 335
 
4.8%
B 310
 
4.5%
C 280
 
4.0%
P 226
 
3.3%
Other values (16) 1660
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 982
14.1%
R 838
12.1%
S 634
 
9.1%
N 624
 
9.0%
U 599
 
8.6%
E 457
 
6.6%
G 335
 
4.8%
B 310
 
4.5%
C 280
 
4.0%
P 226
 
3.3%
Other values (16) 1660
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 982
14.1%
R 838
12.1%
S 634
 
9.1%
N 624
 
9.0%
U 599
 
8.6%
E 457
 
6.6%
G 335
 
4.8%
B 310
 
4.5%
C 280
 
4.0%
P 226
 
3.3%
Other values (16) 1660
23.9%
Distinct92
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2025-03-12T16:35:51.924901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length16
Mean length8.2492441
Min length3

Characters and Unicode

Total characters19097
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.6%

Sample

1st rowBelgium
2nd rowItaly
3rd rowBelgium
4th rowAustralia
5th rowGreat Britain
ValueCountFrequency (%)
united 330
 
11.3%
states 330
 
11.3%
france 187
 
6.4%
china 172
 
5.9%
great 162
 
5.5%
britain 162
 
5.5%
australia 123
 
4.2%
netherlands 118
 
4.0%
germany 113
 
3.9%
italy 88
 
3.0%
Other values (95) 1148
39.1%
2025-03-12T16:35:52.021085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2869
15.0%
e 1862
 
9.8%
t 1785
 
9.3%
n 1728
 
9.0%
i 1501
 
7.9%
r 1349
 
7.1%
d 667
 
3.5%
s 650
 
3.4%
618
 
3.2%
l 569
 
3.0%
Other values (43) 5499
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2869
15.0%
e 1862
 
9.8%
t 1785
 
9.3%
n 1728
 
9.0%
i 1501
 
7.9%
r 1349
 
7.1%
d 667
 
3.5%
s 650
 
3.4%
618
 
3.2%
l 569
 
3.0%
Other values (43) 5499
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2869
15.0%
e 1862
 
9.8%
t 1785
 
9.3%
n 1728
 
9.0%
i 1501
 
7.9%
r 1349
 
7.1%
d 667
 
3.5%
s 650
 
3.4%
618
 
3.2%
l 569
 
3.0%
Other values (43) 5499
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2869
15.0%
e 1862
 
9.8%
t 1785
 
9.3%
n 1728
 
9.0%
i 1501
 
7.9%
r 1349
 
7.1%
d 667
 
3.5%
s 650
 
3.4%
618
 
3.2%
l 569
 
3.0%
Other values (43) 5499
28.8%
Distinct92
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2025-03-12T16:35:52.092494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length20
Mean length11.834125
Min length3

Characters and Unicode

Total characters27396
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.6%

Sample

1st rowBelgium
2nd rowItaly
3rd rowBelgium
4th rowAustralia
5th rowGreat Britain
ValueCountFrequency (%)
of 580
 
13.6%
united 330
 
7.7%
america 330
 
7.7%
states 330
 
7.7%
republic 253
 
5.9%
france 187
 
4.4%
people's 176
 
4.1%
china 172
 
4.0%
great 162
 
3.8%
britain 162
 
3.8%
Other values (99) 1581
37.1%
2025-03-12T16:35:52.192148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3220
 
11.8%
e 2802
 
10.2%
i 2098
 
7.7%
1948
 
7.1%
t 1793
 
6.5%
n 1728
 
6.3%
r 1687
 
6.2%
o 1092
 
4.0%
l 1004
 
3.7%
c 908
 
3.3%
Other values (43) 9116
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3220
 
11.8%
e 2802
 
10.2%
i 2098
 
7.7%
1948
 
7.1%
t 1793
 
6.5%
n 1728
 
6.3%
r 1687
 
6.2%
o 1092
 
4.0%
l 1004
 
3.7%
c 908
 
3.3%
Other values (43) 9116
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3220
 
11.8%
e 2802
 
10.2%
i 2098
 
7.7%
1948
 
7.1%
t 1793
 
6.5%
n 1728
 
6.3%
r 1687
 
6.2%
o 1092
 
4.0%
l 1004
 
3.7%
c 908
 
3.3%
Other values (43) 9116
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3220
 
11.8%
e 2802
 
10.2%
i 2098
 
7.7%
1948
 
7.1%
t 1793
 
6.5%
n 1728
 
6.3%
r 1687
 
6.2%
o 1092
 
4.0%
l 1004
 
3.7%
c 908
 
3.3%
Other values (43) 9116
33.3%
Distinct92
Distinct (%)4.0%
Missing1
Missing (%)< 0.1%
Memory size18.2 KiB
2025-03-12T16:35:52.248504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6942
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.6%

Sample

1st rowBEL
2nd rowITA
3rd rowBEL
4th rowAUS
5th rowGBR
ValueCountFrequency (%)
usa 332
 
14.3%
fra 187
 
8.1%
chn 168
 
7.3%
gbr 162
 
7.0%
aus 123
 
5.3%
ned 117
 
5.1%
ger 113
 
4.9%
ita 88
 
3.8%
esp 83
 
3.6%
jpn 82
 
3.5%
Other values (82) 859
37.1%
2025-03-12T16:35:52.326489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 978
14.1%
R 842
12.1%
S 638
 
9.2%
N 617
 
8.9%
U 601
 
8.7%
E 455
 
6.6%
G 335
 
4.8%
B 314
 
4.5%
C 281
 
4.0%
P 224
 
3.2%
Other values (16) 1657
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 978
14.1%
R 842
12.1%
S 638
 
9.2%
N 617
 
8.9%
U 601
 
8.7%
E 455
 
6.6%
G 335
 
4.8%
B 314
 
4.5%
C 281
 
4.0%
P 224
 
3.2%
Other values (16) 1657
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 978
14.1%
R 842
12.1%
S 638
 
9.2%
N 617
 
8.9%
U 601
 
8.7%
E 455
 
6.6%
G 335
 
4.8%
B 314
 
4.5%
C 281
 
4.0%
P 224
 
3.2%
Other values (16) 1657
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 978
14.1%
R 842
12.1%
S 638
 
9.2%
N 617
 
8.9%
U 601
 
8.7%
E 455
 
6.6%
G 335
 
4.8%
B 314
 
4.5%
C 281
 
4.0%
P 224
 
3.2%
Other values (16) 1657
23.9%
Distinct92
Distinct (%)4.0%
Missing1
Missing (%)< 0.1%
Memory size18.2 KiB
2025-03-12T16:35:52.396273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length16
Mean length8.2718237
Min length4

Characters and Unicode

Total characters19141
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.6%

Sample

1st rowBelgium
2nd rowItaly
3rd rowBelgium
4th rowAustralia
5th rowGreat Britain
ValueCountFrequency (%)
united 332
 
11.3%
states 332
 
11.3%
france 187
 
6.4%
china 172
 
5.9%
great 162
 
5.5%
britain 162
 
5.5%
australia 123
 
4.2%
netherlands 117
 
4.0%
germany 113
 
3.9%
italy 88
 
3.0%
Other values (95) 1146
39.1%
2025-03-12T16:35:52.494643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2879
15.0%
e 1871
 
9.8%
t 1790
 
9.4%
n 1734
 
9.1%
i 1505
 
7.9%
r 1353
 
7.1%
d 670
 
3.5%
s 659
 
3.4%
620
 
3.2%
l 572
 
3.0%
Other values (42) 5488
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19141
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2879
15.0%
e 1871
 
9.8%
t 1790
 
9.4%
n 1734
 
9.1%
i 1505
 
7.9%
r 1353
 
7.1%
d 670
 
3.5%
s 659
 
3.4%
620
 
3.2%
l 572
 
3.0%
Other values (42) 5488
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19141
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2879
15.0%
e 1871
 
9.8%
t 1790
 
9.4%
n 1734
 
9.1%
i 1505
 
7.9%
r 1353
 
7.1%
d 670
 
3.5%
s 659
 
3.4%
620
 
3.2%
l 572
 
3.0%
Other values (42) 5488
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19141
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2879
15.0%
e 1871
 
9.8%
t 1790
 
9.4%
n 1734
 
9.1%
i 1505
 
7.9%
r 1353
 
7.1%
d 670
 
3.5%
s 659
 
3.4%
620
 
3.2%
l 572
 
3.0%
Other values (42) 5488
28.7%
Distinct92
Distinct (%)4.0%
Missing1
Missing (%)< 0.1%
Memory size18.2 KiB
2025-03-12T16:35:52.563378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length19
Mean length11.860415
Min length4

Characters and Unicode

Total characters27445
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.6%

Sample

1st rowBelgium
2nd rowItaly
3rd rowBelgium
4th rowAustralia
5th rowGreat Britain
ValueCountFrequency (%)
of 582
 
13.6%
united 332
 
7.8%
america 332
 
7.8%
states 332
 
7.8%
republic 253
 
5.9%
france 187
 
4.4%
people's 176
 
4.1%
china 172
 
4.0%
great 162
 
3.8%
britain 162
 
3.8%
Other values (97) 1576
36.9%
2025-03-12T16:35:52.666357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3231
 
11.8%
e 2809
 
10.2%
i 2103
 
7.7%
1952
 
7.1%
t 1798
 
6.6%
n 1734
 
6.3%
r 1693
 
6.2%
o 1094
 
4.0%
l 1006
 
3.7%
c 907
 
3.3%
Other values (42) 9118
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3231
 
11.8%
e 2809
 
10.2%
i 2103
 
7.7%
1952
 
7.1%
t 1798
 
6.6%
n 1734
 
6.3%
r 1693
 
6.2%
o 1094
 
4.0%
l 1006
 
3.7%
c 907
 
3.3%
Other values (42) 9118
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3231
 
11.8%
e 2809
 
10.2%
i 2103
 
7.7%
1952
 
7.1%
t 1798
 
6.6%
n 1734
 
6.3%
r 1693
 
6.2%
o 1094
 
4.0%
l 1006
 
3.7%
c 907
 
3.3%
Other values (42) 9118
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3231
 
11.8%
e 2809
 
10.2%
i 2103
 
7.7%
1952
 
7.1%
t 1798
 
6.6%
n 1734
 
6.3%
r 1693
 
6.2%
o 1094
 
4.0%
l 1006
 
3.7%
c 907
 
3.3%
Other values (42) 9118
33.2%

team
Text

Missing 

Distinct101
Distinct (%)6.5%
Missing760
Missing (%)32.8%
Memory size18.2 KiB
2025-03-12T16:35:52.730398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length47
Median length37
Mean length12.73955
Min length4

Characters and Unicode

Total characters19810
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPeople's Republic of China
2nd rowPeople's Republic of China
3rd rowUnited States of America
4th rowUnited States of America
5th rowGreat Britain
ValueCountFrequency (%)
of 354
 
11.1%
united 232
 
7.3%
america 232
 
7.3%
states 232
 
7.3%
france 138
 
4.3%
great 125
 
3.9%
britain 125
 
3.9%
republic 122
 
3.8%
98
 
3.1%
netherlands 98
 
3.1%
Other values (241) 1433
44.9%
2025-03-12T16:35:52.820136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2198
 
11.1%
e 1877
 
9.5%
1634
 
8.2%
i 1427
 
7.2%
t 1274
 
6.4%
r 1233
 
6.2%
n 1221
 
6.2%
o 716
 
3.6%
l 620
 
3.1%
c 599
 
3.0%
Other values (49) 7011
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2198
 
11.1%
e 1877
 
9.5%
1634
 
8.2%
i 1427
 
7.2%
t 1274
 
6.4%
r 1233
 
6.2%
n 1221
 
6.2%
o 716
 
3.6%
l 620
 
3.1%
c 599
 
3.0%
Other values (49) 7011
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2198
 
11.1%
e 1877
 
9.5%
1634
 
8.2%
i 1427
 
7.2%
t 1274
 
6.4%
r 1233
 
6.2%
n 1221
 
6.2%
o 716
 
3.6%
l 620
 
3.1%
c 599
 
3.0%
Other values (49) 7011
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2198
 
11.1%
e 1877
 
9.5%
1634
 
8.2%
i 1427
 
7.2%
t 1274
 
6.4%
r 1233
 
6.2%
n 1221
 
6.2%
o 716
 
3.6%
l 620
 
3.1%
c 599
 
3.0%
Other values (49) 7011
35.4%

team_gender
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.3%
Missing760
Missing (%)32.8%
Memory size18.2 KiB
W
678 
M
650 
X
164 
O
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1555
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowW
3rd rowW
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
W 678
29.3%
M 650
28.1%
X 164
 
7.1%
O 63
 
2.7%
(Missing) 760
32.8%

Length

2025-03-12T16:35:52.903386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T16:35:52.919576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
w 678
43.6%
m 650
41.8%
x 164
 
10.5%
o 63
 
4.1%

Most occurring characters

ValueCountFrequency (%)
W 678
43.6%
M 650
41.8%
X 164
 
10.5%
O 63
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 678
43.6%
M 650
41.8%
X 164
 
10.5%
O 63
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 678
43.6%
M 650
41.8%
X 164
 
10.5%
O 63
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 678
43.6%
M 650
41.8%
X 164
 
10.5%
O 63
 
4.1%

discipline
Categorical

High correlation 

Distinct45
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Athletics
231 
Swimming
219 
Rowing
 
144
Football
 
124
Judo
 
105
Other values (40)
1492 

Length

Max length21
Median length18
Mean length9.2898488
Min length4

Characters and Unicode

Total characters21506
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCycling Road
2nd rowCycling Road
3rd rowCycling Road
4th rowCycling Road
5th rowCycling Road

Common Values

ValueCountFrequency (%)
Athletics 231
 
10.0%
Swimming 219
 
9.5%
Rowing 144
 
6.2%
Football 124
 
5.4%
Judo 105
 
4.5%
Hockey 102
 
4.4%
Handball 94
 
4.1%
Fencing 90
 
3.9%
Cycling Track 87
 
3.8%
Rugby Sevens 78
 
3.4%
Other values (35) 1041
45.0%

Length

2025-03-12T16:35:52.944554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
swimming 258
 
8.9%
athletics 231
 
8.0%
rowing 144
 
5.0%
football 124
 
4.3%
cycling 117
 
4.0%
judo 105
 
3.6%
hockey 102
 
3.5%
artistic 100
 
3.4%
basketball 96
 
3.3%
handball 94
 
3.2%
Other values (43) 1529
52.7%

Most occurring characters

ValueCountFrequency (%)
i 2106
 
9.8%
n 1725
 
8.0%
l 1678
 
7.8%
t 1458
 
6.8%
e 1288
 
6.0%
o 1281
 
6.0%
a 1229
 
5.7%
g 1045
 
4.9%
c 884
 
4.1%
s 864
 
4.0%
Other values (33) 7948
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2106
 
9.8%
n 1725
 
8.0%
l 1678
 
7.8%
t 1458
 
6.8%
e 1288
 
6.0%
o 1281
 
6.0%
a 1229
 
5.7%
g 1045
 
4.9%
c 884
 
4.1%
s 864
 
4.0%
Other values (33) 7948
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2106
 
9.8%
n 1725
 
8.0%
l 1678
 
7.8%
t 1458
 
6.8%
e 1288
 
6.0%
o 1281
 
6.0%
a 1229
 
5.7%
g 1045
 
4.9%
c 884
 
4.1%
s 864
 
4.0%
Other values (33) 7948
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2106
 
9.8%
n 1725
 
8.0%
l 1678
 
7.8%
t 1458
 
6.8%
e 1288
 
6.0%
o 1281
 
6.0%
a 1229
 
5.7%
g 1045
 
4.9%
c 884
 
4.1%
s 864
 
4.0%
Other values (33) 7948
37.0%

event
Text

Distinct288
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2025-03-12T16:35:53.007446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length30
Mean length13.538229
Min length3

Characters and Unicode

Total characters31341
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMen's Individual Time Trial
2nd rowMen's Individual Time Trial
3rd rowMen's Individual Time Trial
4th rowWomen's Individual Time Trial
5th rowWomen's Individual Time Trial
ValueCountFrequency (%)
men's 634
 
11.2%
women's 611
 
10.8%
men 404
 
7.2%
women 400
 
7.1%
team 331
 
5.9%
relay 251
 
4.4%
4 233
 
4.1%
x 233
 
4.1%
mixed 164
 
2.9%
100m 163
 
2.9%
Other values (157) 2217
39.3%
2025-03-12T16:35:53.103873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4042
 
12.9%
3326
 
10.6%
n 2488
 
7.9%
m 1980
 
6.3%
s 1764
 
5.6%
o 1537
 
4.9%
M 1307
 
4.2%
' 1245
 
4.0%
a 1131
 
3.6%
l 1100
 
3.5%
Other values (55) 11421
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4042
 
12.9%
3326
 
10.6%
n 2488
 
7.9%
m 1980
 
6.3%
s 1764
 
5.6%
o 1537
 
4.9%
M 1307
 
4.2%
' 1245
 
4.0%
a 1131
 
3.6%
l 1100
 
3.5%
Other values (55) 11421
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4042
 
12.9%
3326
 
10.6%
n 2488
 
7.9%
m 1980
 
6.3%
s 1764
 
5.6%
o 1537
 
4.9%
M 1307
 
4.2%
' 1245
 
4.0%
a 1131
 
3.6%
l 1100
 
3.5%
Other values (55) 11421
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4042
 
12.9%
3326
 
10.6%
n 2488
 
7.9%
m 1980
 
6.3%
s 1764
 
5.6%
o 1537
 
4.9%
M 1307
 
4.2%
' 1245
 
4.0%
a 1131
 
3.6%
l 1100
 
3.5%
Other values (55) 11421
36.4%

event_type
Categorical

High correlation 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
HTEAM
843 
TEAM
638 
ATH
494 
HATH
266 
HCOUP
 
42

Length

Max length5
Median length4
Mean length4.1688985
Min length3

Characters and Unicode

Total characters9651
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowATH
2nd rowATH
3rd rowATH
4th rowATH
5th rowATH

Common Values

ValueCountFrequency (%)
HTEAM 843
36.4%
TEAM 638
27.6%
ATH 494
21.3%
HATH 266
 
11.5%
HCOUP 42
 
1.8%
COUP 32
 
1.4%

Length

2025-03-12T16:35:53.132100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T16:35:53.153369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hteam 843
36.4%
team 638
27.6%
ath 494
21.3%
hath 266
 
11.5%
hcoup 42
 
1.8%
coup 32
 
1.4%

Most occurring characters

ValueCountFrequency (%)
T 2241
23.2%
A 2241
23.2%
H 1911
19.8%
E 1481
15.3%
M 1481
15.3%
C 74
 
0.8%
O 74
 
0.8%
U 74
 
0.8%
P 74
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2241
23.2%
A 2241
23.2%
H 1911
19.8%
E 1481
15.3%
M 1481
15.3%
C 74
 
0.8%
O 74
 
0.8%
U 74
 
0.8%
P 74
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2241
23.2%
A 2241
23.2%
H 1911
19.8%
E 1481
15.3%
M 1481
15.3%
C 74
 
0.8%
O 74
 
0.8%
U 74
 
0.8%
P 74
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2241
23.2%
A 2241
23.2%
H 1911
19.8%
E 1481
15.3%
M 1481
15.3%
C 74
 
0.8%
O 74
 
0.8%
U 74
 
0.8%
P 74
 
0.8%
Distinct489
Distinct (%)21.3%
Missing21
Missing (%)0.9%
Memory size18.2 KiB
2025-03-12T16:35:53.243652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length79
Median length71
Mean length59.756321
Min length46

Characters and Unicode

Total characters137081
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)5.3%

Sample

1st row/en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100--
2nd row/en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100--
3rd row/en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100--
4th row/en/paris-2024/results/cycling-road/women-s-individual-time-trial/fnl-000100--
5th row/en/paris-2024/results/cycling-road/women-s-individual-time-trial/fnl-000100--
ValueCountFrequency (%)
en/paris-2024/results/football/men/fnl-000100 43
 
1.9%
en/paris-2024/results/football/women/fnl-000100 42
 
1.8%
en/paris-2024/results/hockey/men/fnl-000100 36
 
1.6%
en/paris-2024/results/hockey/women/fnl-000100 34
 
1.5%
en/paris-2024/results/handball/men/fnl-000100 32
 
1.4%
en/paris-2024/results/handball/women/fnl-000100 32
 
1.4%
en/paris-2024/results/judo/mixed-team/fnl-00010000 28
 
1.2%
en/paris-2024/results/rowing/women-s-eight/fnl 27
 
1.2%
en/paris-2024/results/rugby-sevens/men/fnl-000100 27
 
1.2%
en/paris-2024/results/rowing/men-s-eight/fnl 27
 
1.2%
Other values (479) 1966
85.7%
2025-03-12T16:35:53.367226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 15989
 
11.7%
/ 13764
 
10.0%
0 13401
 
9.8%
s 10339
 
7.5%
e 10178
 
7.4%
n 8616
 
6.3%
l 7231
 
5.3%
r 6541
 
4.8%
i 5244
 
3.8%
2 5066
 
3.7%
Other values (28) 40712
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 137081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 15989
 
11.7%
/ 13764
 
10.0%
0 13401
 
9.8%
s 10339
 
7.5%
e 10178
 
7.4%
n 8616
 
6.3%
l 7231
 
5.3%
r 6541
 
4.8%
i 5244
 
3.8%
2 5066
 
3.7%
Other values (28) 40712
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 137081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 15989
 
11.7%
/ 13764
 
10.0%
0 13401
 
9.8%
s 10339
 
7.5%
e 10178
 
7.4%
n 8616
 
6.3%
l 7231
 
5.3%
r 6541
 
4.8%
i 5244
 
3.8%
2 5066
 
3.7%
Other values (28) 40712
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 137081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 15989
 
11.7%
/ 13764
 
10.0%
0 13401
 
9.8%
s 10339
 
7.5%
e 10178
 
7.4%
n 8616
 
6.3%
l 7231
 
5.3%
r 6541
 
4.8%
i 5244
 
3.8%
2 5066
 
3.7%
Other values (28) 40712
29.7%
Distinct1748
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Minimum1965-11-14 00:00:00
Maximum2010-05-12 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T16:35:53.397096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T16:35:53.429398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

code_athlete
Real number (ℝ)

Distinct2054
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1893320.8
Minimum1532872
Maximum4980004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.2 KiB
2025-03-12T16:35:53.462360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1532872
5-th percentile1550169.1
Q11896552
median1924464
Q31950498.5
95-th percentile1971960.9
Maximum4980004
Range3447132
Interquartile range (IQR)53946.5

Descriptive statistics

Standard deviation262827.59
Coefficient of variation (CV)0.13881831
Kurtosis79.186269
Mean1893320.8
Median Absolute Deviation (MAD)27464
Skewness7.2646854
Sum4.3830377 × 109
Variance6.9078343 × 1010
MonotonicityNot monotonic
2025-03-12T16:35:53.495613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1945125 6
 
0.3%
1909294 5
 
0.2%
1946218 5
 
0.2%
1935984 5
 
0.2%
1946198 5
 
0.2%
1935923 5
 
0.2%
1935998 4
 
0.2%
1967140 4
 
0.2%
1935938 4
 
0.2%
1919480 4
 
0.2%
Other values (2044) 2268
98.0%
ValueCountFrequency (%)
1532872 1
< 0.1%
1532873 1
< 0.1%
1535187 1
< 0.1%
1535349 2
0.1%
1535420 1
< 0.1%
1535429 1
< 0.1%
1535513 1
< 0.1%
1535852 1
< 0.1%
1536045 1
< 0.1%
1536058 1
< 0.1%
ValueCountFrequency (%)
4980004 1
< 0.1%
4979564 2
0.1%
4979557 1
< 0.1%
4979555 1
< 0.1%
4975921 1
< 0.1%
4669223 1
< 0.1%
4665325 1
< 0.1%
4663897 1
< 0.1%
4654306 1
< 0.1%
4343340 1
< 0.1%

code_team
Text

Missing 

Distinct284
Distinct (%)18.3%
Missing760
Missing (%)32.8%
Memory size18.2 KiB
2025-03-12T16:35:53.578322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters26435
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIVW3MTEAM2-CHN01
2nd rowDIVW3MTEAM2-CHN01
3rd rowDIVW3MTEAM2-USA01
4th rowDIVW3MTEAM2-USA01
5th rowDIVW3MTEAM2-GBR01
ValueCountFrequency (%)
fblmteam11--esp01 22
 
1.4%
fblwteam11--bra01 22
 
1.4%
fblmteam11--fra01 21
 
1.4%
fblwteam11--ger01 20
 
1.3%
fblwteam11--usa01 20
 
1.3%
fblmteam11--mar01 19
 
1.2%
hocmteam11--ned01 18
 
1.2%
hocmteam11--ger01 18
 
1.2%
hocwteam11--ned01 17
 
1.1%
hocwteam11--chn01 17
 
1.1%
Other values (274) 1361
87.5%
2025-03-12T16:35:53.683547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3151
 
11.9%
M 2258
 
8.5%
0 2139
 
8.1%
1 2135
 
8.1%
A 2007
 
7.6%
E 1718
 
6.5%
T 1375
 
5.2%
R 1164
 
4.4%
W 1104
 
4.2%
S 891
 
3.4%
Other values (27) 8493
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 3151
 
11.9%
M 2258
 
8.5%
0 2139
 
8.1%
1 2135
 
8.1%
A 2007
 
7.6%
E 1718
 
6.5%
T 1375
 
5.2%
R 1164
 
4.4%
W 1104
 
4.2%
S 891
 
3.4%
Other values (27) 8493
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 3151
 
11.9%
M 2258
 
8.5%
0 2139
 
8.1%
1 2135
 
8.1%
A 2007
 
7.6%
E 1718
 
6.5%
T 1375
 
5.2%
R 1164
 
4.4%
W 1104
 
4.2%
S 891
 
3.4%
Other values (27) 8493
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 3151
 
11.9%
M 2258
 
8.5%
0 2139
 
8.1%
1 2135
 
8.1%
A 2007
 
7.6%
E 1718
 
6.5%
T 1375
 
5.2%
R 1164
 
4.4%
W 1104
 
4.2%
S 891
 
3.4%
Other values (27) 8493
32.1%

is_medallist
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
True
2268 
False
 
47
ValueCountFrequency (%)
True 2268
98.0%
False 47
 
2.0%
2025-03-12T16:35:53.702431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-03-12T16:35:51.206741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-12T16:35:53.717368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
code_athletedisciplineevent_typegenderis_medallistmedal_codemedal_datemedal_typeteam_gender
code_athlete1.0000.1410.0850.0970.1770.0730.0570.0730.059
discipline0.1411.0000.7680.0960.2910.0000.4630.0000.698
event_type0.0850.7681.0000.0310.2150.0420.2790.0430.176
gender0.0970.0960.0311.0000.0000.0000.2340.0000.921
is_medallist0.1770.2910.2150.0001.0000.0000.1360.0000.173
medal_code0.0730.0000.0420.0000.0001.0000.0001.0000.000
medal_date0.0570.4630.2790.2340.1360.0001.0000.0000.468
medal_type0.0730.0000.0430.0000.0001.0000.0001.0000.000
team_gender0.0590.6980.1760.9210.1730.0000.4680.0001.000

Missing values

2025-03-12T16:35:51.257001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T16:35:51.304352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-12T16:35:51.358222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

medal_datemedal_typemedal_codenamegendercountry_codecountrycountry_longnationality_codenationalitynationality_longteamteam_genderdisciplineeventevent_typeurl_eventbirth_datecode_athletecode_teamis_medallist
02024-07-27Gold Medal1.0EVENEPOEL RemcoMaleBELBelgiumBelgiumBELBelgiumBelgiumNaNNaNCycling RoadMen's Individual Time TrialATH/en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100--2000-01-251903136NaNTrue
12024-07-27Silver Medal2.0GANNA FilippoMaleITAItalyItalyITAItalyItalyNaNNaNCycling RoadMen's Individual Time TrialATH/en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100--1996-07-251923520NaNTrue
22024-07-27Bronze Medal3.0van AERT WoutMaleBELBelgiumBelgiumBELBelgiumBelgiumNaNNaNCycling RoadMen's Individual Time TrialATH/en/paris-2024/results/cycling-road/men-s-individual-time-trial/fnl-000100--1994-09-151903147NaNTrue
32024-07-27Gold Medal1.0BROWN GraceFemaleAUSAustraliaAustraliaAUSAustraliaAustraliaNaNNaNCycling RoadWomen's Individual Time TrialATH/en/paris-2024/results/cycling-road/women-s-individual-time-trial/fnl-000100--1992-07-071940173NaNTrue
42024-07-27Silver Medal2.0HENDERSON AnnaFemaleGBRGreat BritainGreat BritainGBRGreat BritainGreat BritainNaNNaNCycling RoadWomen's Individual Time TrialATH/en/paris-2024/results/cycling-road/women-s-individual-time-trial/fnl-000100--1998-11-141912525NaNTrue
52024-07-27Bronze Medal3.0DYGERT ChloeFemaleUSAUnited StatesUnited States of AmericaUSAUnited StatesUnited States of AmericaNaNNaNCycling RoadWomen's Individual Time TrialATH/en/paris-2024/results/cycling-road/women-s-individual-time-trial/fnl-000100--1997-01-011955079NaNTrue
62024-07-27Gold Medal1.0OH SangukMaleKORKoreaRepublic of KoreaKORKoreaRepublic of KoreaNaNNaNFencingMen's Sabre IndividualHATH/en/paris-2024/results/fencing/men-s-sabre-individual/fnl-000100--1996-09-301927149NaNTrue
72024-07-27Silver Medal2.0FERJANI FaresMaleTUNTunisiaTunisiaTUNTunisiaTunisiaNaNNaNFencingMen's Sabre IndividualHATH/en/paris-2024/results/fencing/men-s-sabre-individual/fnl-000100--1997-07-221937783NaNTrue
82024-07-27Bronze Medal3.0SAMELE LuigiMaleITAItalyItalyITAItalyItalyNaNNaNFencingMen's Sabre IndividualHATH/en/paris-2024/results/fencing/men-s-sabre-individual/fnl-000200--1987-07-251924595NaNTrue
92024-07-27Gold Medal1.0KONG Man Wai VivianFemaleHKGHong Kong, ChinaHong Kong, ChinaHKGHong Kong, ChinaHong Kong, ChinaNaNNaNFencingWomen's Épée IndividualHATH/en/paris-2024/results/fencing/women-s-epee-individual/fnl-000100--1994-02-081963262NaNTrue
medal_datemedal_typemedal_codenamegendercountry_codecountrycountry_longnationality_codenationalitynationality_longteamteam_genderdisciplineeventevent_typeurl_eventbirth_datecode_athletecode_teamis_medallist
23052024-08-10Silver Medal2.0BAKANOV ShaniFemaleISRIsraelIsraelISRIsraelIsraelIsraelWRhythmic GymnasticsGroup All-AroundTEAMNaN2006-02-271908226GRYW5AA-----ISR01True
23062024-08-10Silver Medal2.0FRIEDMANN AdarFemaleISRIsraelIsraelISRIsraelIsraelIsraelWRhythmic GymnasticsGroup All-AroundTEAMNaN2006-07-181908240GRYW5AA-----ISR01True
23072024-08-10Silver Medal2.0PARITZKI RomiFemaleISRIsraelIsraelISRIsraelIsraelIsraelWRhythmic GymnasticsGroup All-AroundTEAMNaN2004-06-171908233GRYW5AA-----ISR01True
23082024-08-10Silver Medal2.0SHAHAM OfirFemaleISRIsraelIsraelISRIsraelIsraelIsraelWRhythmic GymnasticsGroup All-AroundTEAMNaN2004-11-231908235GRYW5AA-----ISR01True
23092024-08-10Silver Medal2.0SVERTSOV DianaFemaleISRIsraelIsraelISRIsraelIsraelIsraelWRhythmic GymnasticsGroup All-AroundTEAMNaN2004-11-151908230GRYW5AA-----ISR01True
23102024-08-10Bronze Medal3.0CENTOFANTI MartinaFemaleITAItalyItalyITAItalyItalyItalyWRhythmic GymnasticsGroup All-AroundTEAMNaN1998-05-191923700GRYW5AA-----ITA01True
23112024-08-10Bronze Medal3.0DURANTI AgneseFemaleITAItalyItalyITAItalyItalyItalyWRhythmic GymnasticsGroup All-AroundTEAMNaN2000-12-181923701GRYW5AA-----ITA01True
23122024-08-10Bronze Medal3.0MAURELLI AlessiaFemaleITAItalyItalyITAItalyItalyItalyWRhythmic GymnasticsGroup All-AroundTEAMNaN1996-08-221923699GRYW5AA-----ITA01True
23132024-08-10Bronze Medal3.0MOGUREAN DanielaFemaleITAItalyItalyITAItalyItalyItalyWRhythmic GymnasticsGroup All-AroundTEAMNaN2001-07-161923703GRYW5AA-----ITA01True
23142024-08-10Bronze Medal3.0PARIS LauraFemaleITAItalyItalyITAItalyItalyItalyWRhythmic GymnasticsGroup All-AroundTEAMNaN2002-09-071923704GRYW5AA-----ITA01True